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Parenthood Support Group

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  • There is no mandatory textbook for the course. We will provide lecture notes, or reading from books. Some good books include: The Design and Analysis of Algorithms by Dexter Kozen: CMU Access via SpringerLink.

  • Algorithm Design by Kleinberg and Tardos: CMU library, slides by Kevin Wayne.

  • Algorithms by Dasgupta, Papadimitriou, and Vazirani (DPV): CMU library, author's site.

  • Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (CLRS): CMU library with links to e-copy.

  • Randomized Algorithms by Motwani and Raghavan: CMU Access to e-copy.

  • Algorithms by Jeff Erickson: online PDF.

  • Design and Analysis of Computer Algorithms by Aho, Hopcroft, and Ullman: CMU library.

  • We assume basic discrete mathematics (counting, basic probability, basic graphs theory, basic linear algebra): some resources include: (15-251) Great Theoretical Ideas in CS: slides from our undergraduate course.

  • (15-151) Discrete Mathematics: our undergraduate course page (contains the textbook).

  • Mathematics for Computer Science by Lehman, Leighton, and Meyer: lecture notes from MIT.

  • A useful stackexchange thread on good linear algebra sources (with links to several free texts).

  • A primer on matrices (by Shephen Boyd), and a linear algebra review (from Stanford's cs229, by our own Zico Kolter) with some multivariate calculus too.

  • Some helpful videos on linear algebra (thanks Anil!).

Material Science Book By Raghavan Pdf Free 750



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